Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Friday 10 July 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 171,895 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 171,885 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 0
## 130 2020-07-08 East of England 0
## 131 2020-03-01 London 0
## 132 2020-03-02 London 0
## 133 2020-03-03 London 0
## 134 2020-03-04 London 0
## 135 2020-03-05 London 0
## 136 2020-03-06 London 1
## 137 2020-03-07 London 0
## 138 2020-03-08 London 0
## 139 2020-03-09 London 1
## 140 2020-03-10 London 0
## 141 2020-03-11 London 5
## 142 2020-03-12 London 6
## 143 2020-03-13 London 10
## 144 2020-03-14 London 13
## 145 2020-03-15 London 9
## 146 2020-03-16 London 15
## 147 2020-03-17 London 23
## 148 2020-03-18 London 27
## 149 2020-03-19 London 25
## 150 2020-03-20 London 44
## 151 2020-03-21 London 49
## 152 2020-03-22 London 54
## 153 2020-03-23 London 63
## 154 2020-03-24 London 86
## 155 2020-03-25 London 112
## 156 2020-03-26 London 129
## 157 2020-03-27 London 129
## 158 2020-03-28 London 122
## 159 2020-03-29 London 145
## 160 2020-03-30 London 149
## 161 2020-03-31 London 181
## 162 2020-04-01 London 202
## 163 2020-04-02 London 191
## 164 2020-04-03 London 196
## 165 2020-04-04 London 230
## 166 2020-04-05 London 195
## 167 2020-04-06 London 197
## 168 2020-04-07 London 220
## 169 2020-04-08 London 238
## 170 2020-04-09 London 206
## 171 2020-04-10 London 170
## 172 2020-04-11 London 178
## 173 2020-04-12 London 158
## 174 2020-04-13 London 166
## 175 2020-04-14 London 143
## 176 2020-04-15 London 142
## 177 2020-04-16 London 140
## 178 2020-04-17 London 100
## 179 2020-04-18 London 101
## 180 2020-04-19 London 103
## 181 2020-04-20 London 95
## 182 2020-04-21 London 94
## 183 2020-04-22 London 109
## 184 2020-04-23 London 77
## 185 2020-04-24 London 71
## 186 2020-04-25 London 58
## 187 2020-04-26 London 53
## 188 2020-04-27 London 51
## 189 2020-04-28 London 44
## 190 2020-04-29 London 45
## 191 2020-04-30 London 40
## 192 2020-05-01 London 41
## 193 2020-05-02 London 41
## 194 2020-05-03 London 36
## 195 2020-05-04 London 30
## 196 2020-05-05 London 25
## 197 2020-05-06 London 37
## 198 2020-05-07 London 37
## 199 2020-05-08 London 30
## 200 2020-05-09 London 23
## 201 2020-05-10 London 26
## 202 2020-05-11 London 18
## 203 2020-05-12 London 18
## 204 2020-05-13 London 17
## 205 2020-05-14 London 20
## 206 2020-05-15 London 18
## 207 2020-05-16 London 14
## 208 2020-05-17 London 15
## 209 2020-05-18 London 10
## 210 2020-05-19 London 14
## 211 2020-05-20 London 19
## 212 2020-05-21 London 12
## 213 2020-05-22 London 10
## 214 2020-05-23 London 6
## 215 2020-05-24 London 7
## 216 2020-05-25 London 9
## 217 2020-05-26 London 13
## 218 2020-05-27 London 7
## 219 2020-05-28 London 8
## 220 2020-05-29 London 7
## 221 2020-05-30 London 12
## 222 2020-05-31 London 6
## 223 2020-06-01 London 10
## 224 2020-06-02 London 8
## 225 2020-06-03 London 6
## 226 2020-06-04 London 8
## 227 2020-06-05 London 4
## 228 2020-06-06 London 0
## 229 2020-06-07 London 5
## 230 2020-06-08 London 5
## 231 2020-06-09 London 4
## 232 2020-06-10 London 7
## 233 2020-06-11 London 5
## 234 2020-06-12 London 3
## 235 2020-06-13 London 3
## 236 2020-06-14 London 3
## 237 2020-06-15 London 1
## 238 2020-06-16 London 2
## 239 2020-06-17 London 1
## 240 2020-06-18 London 2
## 241 2020-06-19 London 5
## 242 2020-06-20 London 3
## 243 2020-06-21 London 4
## 244 2020-06-22 London 2
## 245 2020-06-23 London 1
## 246 2020-06-24 London 4
## 247 2020-06-25 London 3
## 248 2020-06-26 London 2
## 249 2020-06-27 London 1
## 250 2020-06-28 London 2
## 251 2020-06-29 London 2
## 252 2020-06-30 London 1
## 253 2020-07-01 London 2
## 254 2020-07-02 London 2
## 255 2020-07-03 London 2
## 256 2020-07-04 London 1
## 257 2020-07-05 London 3
## 258 2020-07-06 London 1
## 259 2020-07-07 London 1
## 260 2020-07-08 London 0
## 261 2020-03-01 Midlands 0
## 262 2020-03-02 Midlands 0
## 263 2020-03-03 Midlands 1
## 264 2020-03-04 Midlands 0
## 265 2020-03-05 Midlands 0
## 266 2020-03-06 Midlands 0
## 267 2020-03-07 Midlands 0
## 268 2020-03-08 Midlands 2
## 269 2020-03-09 Midlands 1
## 270 2020-03-10 Midlands 0
## 271 2020-03-11 Midlands 2
## 272 2020-03-12 Midlands 6
## 273 2020-03-13 Midlands 5
## 274 2020-03-14 Midlands 4
## 275 2020-03-15 Midlands 5
## 276 2020-03-16 Midlands 11
## 277 2020-03-17 Midlands 8
## 278 2020-03-18 Midlands 13
## 279 2020-03-19 Midlands 8
## 280 2020-03-20 Midlands 28
## 281 2020-03-21 Midlands 13
## 282 2020-03-22 Midlands 31
## 283 2020-03-23 Midlands 33
## 284 2020-03-24 Midlands 41
## 285 2020-03-25 Midlands 48
## 286 2020-03-26 Midlands 64
## 287 2020-03-27 Midlands 72
## 288 2020-03-28 Midlands 89
## 289 2020-03-29 Midlands 92
## 290 2020-03-30 Midlands 90
## 291 2020-03-31 Midlands 123
## 292 2020-04-01 Midlands 140
## 293 2020-04-02 Midlands 142
## 294 2020-04-03 Midlands 124
## 295 2020-04-04 Midlands 151
## 296 2020-04-05 Midlands 164
## 297 2020-04-06 Midlands 140
## 298 2020-04-07 Midlands 123
## 299 2020-04-08 Midlands 186
## 300 2020-04-09 Midlands 139
## 301 2020-04-10 Midlands 127
## 302 2020-04-11 Midlands 142
## 303 2020-04-12 Midlands 139
## 304 2020-04-13 Midlands 120
## 305 2020-04-14 Midlands 116
## 306 2020-04-15 Midlands 147
## 307 2020-04-16 Midlands 102
## 308 2020-04-17 Midlands 118
## 309 2020-04-18 Midlands 115
## 310 2020-04-19 Midlands 92
## 311 2020-04-20 Midlands 107
## 312 2020-04-21 Midlands 86
## 313 2020-04-22 Midlands 78
## 314 2020-04-23 Midlands 103
## 315 2020-04-24 Midlands 79
## 316 2020-04-25 Midlands 72
## 317 2020-04-26 Midlands 81
## 318 2020-04-27 Midlands 74
## 319 2020-04-28 Midlands 68
## 320 2020-04-29 Midlands 53
## 321 2020-04-30 Midlands 56
## 322 2020-05-01 Midlands 64
## 323 2020-05-02 Midlands 51
## 324 2020-05-03 Midlands 52
## 325 2020-05-04 Midlands 61
## 326 2020-05-05 Midlands 59
## 327 2020-05-06 Midlands 59
## 328 2020-05-07 Midlands 48
## 329 2020-05-08 Midlands 34
## 330 2020-05-09 Midlands 37
## 331 2020-05-10 Midlands 42
## 332 2020-05-11 Midlands 33
## 333 2020-05-12 Midlands 45
## 334 2020-05-13 Midlands 40
## 335 2020-05-14 Midlands 37
## 336 2020-05-15 Midlands 40
## 337 2020-05-16 Midlands 34
## 338 2020-05-17 Midlands 31
## 339 2020-05-18 Midlands 35
## 340 2020-05-19 Midlands 34
## 341 2020-05-20 Midlands 36
## 342 2020-05-21 Midlands 32
## 343 2020-05-22 Midlands 27
## 344 2020-05-23 Midlands 34
## 345 2020-05-24 Midlands 20
## 346 2020-05-25 Midlands 26
## 347 2020-05-26 Midlands 33
## 348 2020-05-27 Midlands 29
## 349 2020-05-28 Midlands 28
## 350 2020-05-29 Midlands 20
## 351 2020-05-30 Midlands 21
## 352 2020-05-31 Midlands 22
## 353 2020-06-01 Midlands 20
## 354 2020-06-02 Midlands 22
## 355 2020-06-03 Midlands 24
## 356 2020-06-04 Midlands 16
## 357 2020-06-05 Midlands 21
## 358 2020-06-06 Midlands 20
## 359 2020-06-07 Midlands 17
## 360 2020-06-08 Midlands 16
## 361 2020-06-09 Midlands 18
## 362 2020-06-10 Midlands 15
## 363 2020-06-11 Midlands 13
## 364 2020-06-12 Midlands 12
## 365 2020-06-13 Midlands 6
## 366 2020-06-14 Midlands 18
## 367 2020-06-15 Midlands 12
## 368 2020-06-16 Midlands 15
## 369 2020-06-17 Midlands 10
## 370 2020-06-18 Midlands 15
## 371 2020-06-19 Midlands 10
## 372 2020-06-20 Midlands 15
## 373 2020-06-21 Midlands 13
## 374 2020-06-22 Midlands 13
## 375 2020-06-23 Midlands 16
## 376 2020-06-24 Midlands 15
## 377 2020-06-25 Midlands 18
## 378 2020-06-26 Midlands 5
## 379 2020-06-27 Midlands 5
## 380 2020-06-28 Midlands 6
## 381 2020-06-29 Midlands 6
## 382 2020-06-30 Midlands 6
## 383 2020-07-01 Midlands 7
## 384 2020-07-02 Midlands 9
## 385 2020-07-03 Midlands 3
## 386 2020-07-04 Midlands 4
## 387 2020-07-05 Midlands 6
## 388 2020-07-06 Midlands 5
## 389 2020-07-07 Midlands 1
## 390 2020-07-08 Midlands 2
## 391 2020-03-01 North East and Yorkshire 0
## 392 2020-03-02 North East and Yorkshire 0
## 393 2020-03-03 North East and Yorkshire 0
## 394 2020-03-04 North East and Yorkshire 0
## 395 2020-03-05 North East and Yorkshire 0
## 396 2020-03-06 North East and Yorkshire 0
## 397 2020-03-07 North East and Yorkshire 0
## 398 2020-03-08 North East and Yorkshire 0
## 399 2020-03-09 North East and Yorkshire 0
## 400 2020-03-10 North East and Yorkshire 0
## 401 2020-03-11 North East and Yorkshire 0
## 402 2020-03-12 North East and Yorkshire 0
## 403 2020-03-13 North East and Yorkshire 0
## 404 2020-03-14 North East and Yorkshire 0
## 405 2020-03-15 North East and Yorkshire 2
## 406 2020-03-16 North East and Yorkshire 3
## 407 2020-03-17 North East and Yorkshire 1
## 408 2020-03-18 North East and Yorkshire 2
## 409 2020-03-19 North East and Yorkshire 6
## 410 2020-03-20 North East and Yorkshire 5
## 411 2020-03-21 North East and Yorkshire 6
## 412 2020-03-22 North East and Yorkshire 7
## 413 2020-03-23 North East and Yorkshire 9
## 414 2020-03-24 North East and Yorkshire 8
## 415 2020-03-25 North East and Yorkshire 18
## 416 2020-03-26 North East and Yorkshire 21
## 417 2020-03-27 North East and Yorkshire 28
## 418 2020-03-28 North East and Yorkshire 35
## 419 2020-03-29 North East and Yorkshire 38
## 420 2020-03-30 North East and Yorkshire 64
## 421 2020-03-31 North East and Yorkshire 60
## 422 2020-04-01 North East and Yorkshire 67
## 423 2020-04-02 North East and Yorkshire 75
## 424 2020-04-03 North East and Yorkshire 100
## 425 2020-04-04 North East and Yorkshire 105
## 426 2020-04-05 North East and Yorkshire 92
## 427 2020-04-06 North East and Yorkshire 96
## 428 2020-04-07 North East and Yorkshire 102
## 429 2020-04-08 North East and Yorkshire 107
## 430 2020-04-09 North East and Yorkshire 111
## 431 2020-04-10 North East and Yorkshire 117
## 432 2020-04-11 North East and Yorkshire 98
## 433 2020-04-12 North East and Yorkshire 84
## 434 2020-04-13 North East and Yorkshire 94
## 435 2020-04-14 North East and Yorkshire 107
## 436 2020-04-15 North East and Yorkshire 96
## 437 2020-04-16 North East and Yorkshire 103
## 438 2020-04-17 North East and Yorkshire 88
## 439 2020-04-18 North East and Yorkshire 95
## 440 2020-04-19 North East and Yorkshire 88
## 441 2020-04-20 North East and Yorkshire 100
## 442 2020-04-21 North East and Yorkshire 76
## 443 2020-04-22 North East and Yorkshire 84
## 444 2020-04-23 North East and Yorkshire 63
## 445 2020-04-24 North East and Yorkshire 72
## 446 2020-04-25 North East and Yorkshire 69
## 447 2020-04-26 North East and Yorkshire 65
## 448 2020-04-27 North East and Yorkshire 65
## 449 2020-04-28 North East and Yorkshire 57
## 450 2020-04-29 North East and Yorkshire 69
## 451 2020-04-30 North East and Yorkshire 57
## 452 2020-05-01 North East and Yorkshire 64
## 453 2020-05-02 North East and Yorkshire 48
## 454 2020-05-03 North East and Yorkshire 40
## 455 2020-05-04 North East and Yorkshire 49
## 456 2020-05-05 North East and Yorkshire 40
## 457 2020-05-06 North East and Yorkshire 51
## 458 2020-05-07 North East and Yorkshire 45
## 459 2020-05-08 North East and Yorkshire 42
## 460 2020-05-09 North East and Yorkshire 44
## 461 2020-05-10 North East and Yorkshire 40
## 462 2020-05-11 North East and Yorkshire 29
## 463 2020-05-12 North East and Yorkshire 27
## 464 2020-05-13 North East and Yorkshire 28
## 465 2020-05-14 North East and Yorkshire 31
## 466 2020-05-15 North East and Yorkshire 32
## 467 2020-05-16 North East and Yorkshire 35
## 468 2020-05-17 North East and Yorkshire 26
## 469 2020-05-18 North East and Yorkshire 30
## 470 2020-05-19 North East and Yorkshire 27
## 471 2020-05-20 North East and Yorkshire 22
## 472 2020-05-21 North East and Yorkshire 33
## 473 2020-05-22 North East and Yorkshire 22
## 474 2020-05-23 North East and Yorkshire 18
## 475 2020-05-24 North East and Yorkshire 26
## 476 2020-05-25 North East and Yorkshire 21
## 477 2020-05-26 North East and Yorkshire 21
## 478 2020-05-27 North East and Yorkshire 22
## 479 2020-05-28 North East and Yorkshire 21
## 480 2020-05-29 North East and Yorkshire 25
## 481 2020-05-30 North East and Yorkshire 20
## 482 2020-05-31 North East and Yorkshire 20
## 483 2020-06-01 North East and Yorkshire 17
## 484 2020-06-02 North East and Yorkshire 23
## 485 2020-06-03 North East and Yorkshire 23
## 486 2020-06-04 North East and Yorkshire 17
## 487 2020-06-05 North East and Yorkshire 18
## 488 2020-06-06 North East and Yorkshire 21
## 489 2020-06-07 North East and Yorkshire 14
## 490 2020-06-08 North East and Yorkshire 11
## 491 2020-06-09 North East and Yorkshire 12
## 492 2020-06-10 North East and Yorkshire 19
## 493 2020-06-11 North East and Yorkshire 7
## 494 2020-06-12 North East and Yorkshire 9
## 495 2020-06-13 North East and Yorkshire 10
## 496 2020-06-14 North East and Yorkshire 11
## 497 2020-06-15 North East and Yorkshire 9
## 498 2020-06-16 North East and Yorkshire 10
## 499 2020-06-17 North East and Yorkshire 9
## 500 2020-06-18 North East and Yorkshire 11
## 501 2020-06-19 North East and Yorkshire 6
## 502 2020-06-20 North East and Yorkshire 4
## 503 2020-06-21 North East and Yorkshire 4
## 504 2020-06-22 North East and Yorkshire 6
## 505 2020-06-23 North East and Yorkshire 7
## 506 2020-06-24 North East and Yorkshire 10
## 507 2020-06-25 North East and Yorkshire 4
## 508 2020-06-26 North East and Yorkshire 7
## 509 2020-06-27 North East and Yorkshire 3
## 510 2020-06-28 North East and Yorkshire 5
## 511 2020-06-29 North East and Yorkshire 2
## 512 2020-06-30 North East and Yorkshire 4
## 513 2020-07-01 North East and Yorkshire 1
## 514 2020-07-02 North East and Yorkshire 4
## 515 2020-07-03 North East and Yorkshire 3
## 516 2020-07-04 North East and Yorkshire 4
## 517 2020-07-05 North East and Yorkshire 2
## 518 2020-07-06 North East and Yorkshire 2
## 519 2020-07-07 North East and Yorkshire 3
## 520 2020-07-08 North East and Yorkshire 0
## 521 2020-03-01 North West 0
## 522 2020-03-02 North West 0
## 523 2020-03-03 North West 0
## 524 2020-03-04 North West 0
## 525 2020-03-05 North West 1
## 526 2020-03-06 North West 0
## 527 2020-03-07 North West 0
## 528 2020-03-08 North West 1
## 529 2020-03-09 North West 0
## 530 2020-03-10 North West 0
## 531 2020-03-11 North West 0
## 532 2020-03-12 North West 2
## 533 2020-03-13 North West 3
## 534 2020-03-14 North West 1
## 535 2020-03-15 North West 4
## 536 2020-03-16 North West 2
## 537 2020-03-17 North West 4
## 538 2020-03-18 North West 6
## 539 2020-03-19 North West 7
## 540 2020-03-20 North West 10
## 541 2020-03-21 North West 11
## 542 2020-03-22 North West 13
## 543 2020-03-23 North West 15
## 544 2020-03-24 North West 21
## 545 2020-03-25 North West 21
## 546 2020-03-26 North West 29
## 547 2020-03-27 North West 36
## 548 2020-03-28 North West 28
## 549 2020-03-29 North West 46
## 550 2020-03-30 North West 67
## 551 2020-03-31 North West 52
## 552 2020-04-01 North West 86
## 553 2020-04-02 North West 96
## 554 2020-04-03 North West 95
## 555 2020-04-04 North West 98
## 556 2020-04-05 North West 102
## 557 2020-04-06 North West 100
## 558 2020-04-07 North West 135
## 559 2020-04-08 North West 127
## 560 2020-04-09 North West 119
## 561 2020-04-10 North West 117
## 562 2020-04-11 North West 138
## 563 2020-04-12 North West 125
## 564 2020-04-13 North West 129
## 565 2020-04-14 North West 131
## 566 2020-04-15 North West 114
## 567 2020-04-16 North West 135
## 568 2020-04-17 North West 98
## 569 2020-04-18 North West 113
## 570 2020-04-19 North West 71
## 571 2020-04-20 North West 83
## 572 2020-04-21 North West 76
## 573 2020-04-22 North West 86
## 574 2020-04-23 North West 85
## 575 2020-04-24 North West 66
## 576 2020-04-25 North West 66
## 577 2020-04-26 North West 55
## 578 2020-04-27 North West 54
## 579 2020-04-28 North West 57
## 580 2020-04-29 North West 63
## 581 2020-04-30 North West 59
## 582 2020-05-01 North West 45
## 583 2020-05-02 North West 56
## 584 2020-05-03 North West 55
## 585 2020-05-04 North West 48
## 586 2020-05-05 North West 48
## 587 2020-05-06 North West 44
## 588 2020-05-07 North West 49
## 589 2020-05-08 North West 42
## 590 2020-05-09 North West 30
## 591 2020-05-10 North West 42
## 592 2020-05-11 North West 35
## 593 2020-05-12 North West 38
## 594 2020-05-13 North West 25
## 595 2020-05-14 North West 26
## 596 2020-05-15 North West 33
## 597 2020-05-16 North West 32
## 598 2020-05-17 North West 24
## 599 2020-05-18 North West 31
## 600 2020-05-19 North West 35
## 601 2020-05-20 North West 27
## 602 2020-05-21 North West 27
## 603 2020-05-22 North West 26
## 604 2020-05-23 North West 31
## 605 2020-05-24 North West 26
## 606 2020-05-25 North West 31
## 607 2020-05-26 North West 27
## 608 2020-05-27 North West 27
## 609 2020-05-28 North West 28
## 610 2020-05-29 North West 20
## 611 2020-05-30 North West 19
## 612 2020-05-31 North West 13
## 613 2020-06-01 North West 12
## 614 2020-06-02 North West 27
## 615 2020-06-03 North West 22
## 616 2020-06-04 North West 22
## 617 2020-06-05 North West 16
## 618 2020-06-06 North West 26
## 619 2020-06-07 North West 20
## 620 2020-06-08 North West 23
## 621 2020-06-09 North West 17
## 622 2020-06-10 North West 16
## 623 2020-06-11 North West 16
## 624 2020-06-12 North West 11
## 625 2020-06-13 North West 10
## 626 2020-06-14 North West 15
## 627 2020-06-15 North West 16
## 628 2020-06-16 North West 15
## 629 2020-06-17 North West 12
## 630 2020-06-18 North West 13
## 631 2020-06-19 North West 7
## 632 2020-06-20 North West 11
## 633 2020-06-21 North West 7
## 634 2020-06-22 North West 11
## 635 2020-06-23 North West 13
## 636 2020-06-24 North West 13
## 637 2020-06-25 North West 15
## 638 2020-06-26 North West 5
## 639 2020-06-27 North West 7
## 640 2020-06-28 North West 9
## 641 2020-06-29 North West 6
## 642 2020-06-30 North West 6
## 643 2020-07-01 North West 2
## 644 2020-07-02 North West 6
## 645 2020-07-03 North West 5
## 646 2020-07-04 North West 2
## 647 2020-07-05 North West 3
## 648 2020-07-06 North West 9
## 649 2020-07-07 North West 3
## 650 2020-07-08 North West 2
## 651 2020-03-01 South East 0
## 652 2020-03-02 South East 0
## 653 2020-03-03 South East 1
## 654 2020-03-04 South East 0
## 655 2020-03-05 South East 1
## 656 2020-03-06 South East 0
## 657 2020-03-07 South East 0
## 658 2020-03-08 South East 1
## 659 2020-03-09 South East 1
## 660 2020-03-10 South East 1
## 661 2020-03-11 South East 1
## 662 2020-03-12 South East 0
## 663 2020-03-13 South East 1
## 664 2020-03-14 South East 1
## 665 2020-03-15 South East 5
## 666 2020-03-16 South East 8
## 667 2020-03-17 South East 7
## 668 2020-03-18 South East 10
## 669 2020-03-19 South East 9
## 670 2020-03-20 South East 13
## 671 2020-03-21 South East 7
## 672 2020-03-22 South East 25
## 673 2020-03-23 South East 20
## 674 2020-03-24 South East 22
## 675 2020-03-25 South East 29
## 676 2020-03-26 South East 35
## 677 2020-03-27 South East 34
## 678 2020-03-28 South East 36
## 679 2020-03-29 South East 55
## 680 2020-03-30 South East 58
## 681 2020-03-31 South East 65
## 682 2020-04-01 South East 66
## 683 2020-04-02 South East 55
## 684 2020-04-03 South East 72
## 685 2020-04-04 South East 80
## 686 2020-04-05 South East 82
## 687 2020-04-06 South East 88
## 688 2020-04-07 South East 100
## 689 2020-04-08 South East 83
## 690 2020-04-09 South East 104
## 691 2020-04-10 South East 88
## 692 2020-04-11 South East 88
## 693 2020-04-12 South East 88
## 694 2020-04-13 South East 84
## 695 2020-04-14 South East 65
## 696 2020-04-15 South East 72
## 697 2020-04-16 South East 56
## 698 2020-04-17 South East 86
## 699 2020-04-18 South East 57
## 700 2020-04-19 South East 70
## 701 2020-04-20 South East 87
## 702 2020-04-21 South East 51
## 703 2020-04-22 South East 54
## 704 2020-04-23 South East 57
## 705 2020-04-24 South East 64
## 706 2020-04-25 South East 51
## 707 2020-04-26 South East 51
## 708 2020-04-27 South East 40
## 709 2020-04-28 South East 40
## 710 2020-04-29 South East 47
## 711 2020-04-30 South East 29
## 712 2020-05-01 South East 37
## 713 2020-05-02 South East 36
## 714 2020-05-03 South East 17
## 715 2020-05-04 South East 35
## 716 2020-05-05 South East 29
## 717 2020-05-06 South East 25
## 718 2020-05-07 South East 27
## 719 2020-05-08 South East 26
## 720 2020-05-09 South East 28
## 721 2020-05-10 South East 19
## 722 2020-05-11 South East 25
## 723 2020-05-12 South East 27
## 724 2020-05-13 South East 18
## 725 2020-05-14 South East 32
## 726 2020-05-15 South East 25
## 727 2020-05-16 South East 22
## 728 2020-05-17 South East 18
## 729 2020-05-18 South East 22
## 730 2020-05-19 South East 12
## 731 2020-05-20 South East 22
## 732 2020-05-21 South East 15
## 733 2020-05-22 South East 17
## 734 2020-05-23 South East 21
## 735 2020-05-24 South East 17
## 736 2020-05-25 South East 13
## 737 2020-05-26 South East 19
## 738 2020-05-27 South East 18
## 739 2020-05-28 South East 12
## 740 2020-05-29 South East 21
## 741 2020-05-30 South East 8
## 742 2020-05-31 South East 12
## 743 2020-06-01 South East 11
## 744 2020-06-02 South East 13
## 745 2020-06-03 South East 18
## 746 2020-06-04 South East 11
## 747 2020-06-05 South East 11
## 748 2020-06-06 South East 10
## 749 2020-06-07 South East 12
## 750 2020-06-08 South East 8
## 751 2020-06-09 South East 10
## 752 2020-06-10 South East 11
## 753 2020-06-11 South East 5
## 754 2020-06-12 South East 6
## 755 2020-06-13 South East 6
## 756 2020-06-14 South East 7
## 757 2020-06-15 South East 8
## 758 2020-06-16 South East 12
## 759 2020-06-17 South East 9
## 760 2020-06-18 South East 4
## 761 2020-06-19 South East 7
## 762 2020-06-20 South East 5
## 763 2020-06-21 South East 3
## 764 2020-06-22 South East 2
## 765 2020-06-23 South East 8
## 766 2020-06-24 South East 7
## 767 2020-06-25 South East 5
## 768 2020-06-26 South East 8
## 769 2020-06-27 South East 8
## 770 2020-06-28 South East 6
## 771 2020-06-29 South East 5
## 772 2020-06-30 South East 5
## 773 2020-07-01 South East 2
## 774 2020-07-02 South East 6
## 775 2020-07-03 South East 3
## 776 2020-07-04 South East 5
## 777 2020-07-05 South East 3
## 778 2020-07-06 South East 3
## 779 2020-07-07 South East 4
## 780 2020-07-08 South East 0
## 781 2020-03-01 South West 0
## 782 2020-03-02 South West 0
## 783 2020-03-03 South West 0
## 784 2020-03-04 South West 0
## 785 2020-03-05 South West 0
## 786 2020-03-06 South West 0
## 787 2020-03-07 South West 0
## 788 2020-03-08 South West 0
## 789 2020-03-09 South West 0
## 790 2020-03-10 South West 0
## 791 2020-03-11 South West 1
## 792 2020-03-12 South West 0
## 793 2020-03-13 South West 0
## 794 2020-03-14 South West 1
## 795 2020-03-15 South West 0
## 796 2020-03-16 South West 0
## 797 2020-03-17 South West 2
## 798 2020-03-18 South West 2
## 799 2020-03-19 South West 4
## 800 2020-03-20 South West 3
## 801 2020-03-21 South West 6
## 802 2020-03-22 South West 7
## 803 2020-03-23 South West 8
## 804 2020-03-24 South West 7
## 805 2020-03-25 South West 9
## 806 2020-03-26 South West 11
## 807 2020-03-27 South West 13
## 808 2020-03-28 South West 21
## 809 2020-03-29 South West 18
## 810 2020-03-30 South West 23
## 811 2020-03-31 South West 23
## 812 2020-04-01 South West 21
## 813 2020-04-02 South West 23
## 814 2020-04-03 South West 30
## 815 2020-04-04 South West 42
## 816 2020-04-05 South West 32
## 817 2020-04-06 South West 34
## 818 2020-04-07 South West 39
## 819 2020-04-08 South West 47
## 820 2020-04-09 South West 24
## 821 2020-04-10 South West 46
## 822 2020-04-11 South West 43
## 823 2020-04-12 South West 23
## 824 2020-04-13 South West 27
## 825 2020-04-14 South West 24
## 826 2020-04-15 South West 32
## 827 2020-04-16 South West 29
## 828 2020-04-17 South West 33
## 829 2020-04-18 South West 25
## 830 2020-04-19 South West 31
## 831 2020-04-20 South West 26
## 832 2020-04-21 South West 26
## 833 2020-04-22 South West 23
## 834 2020-04-23 South West 17
## 835 2020-04-24 South West 19
## 836 2020-04-25 South West 15
## 837 2020-04-26 South West 27
## 838 2020-04-27 South West 13
## 839 2020-04-28 South West 17
## 840 2020-04-29 South West 15
## 841 2020-04-30 South West 26
## 842 2020-05-01 South West 6
## 843 2020-05-02 South West 7
## 844 2020-05-03 South West 10
## 845 2020-05-04 South West 17
## 846 2020-05-05 South West 14
## 847 2020-05-06 South West 19
## 848 2020-05-07 South West 16
## 849 2020-05-08 South West 6
## 850 2020-05-09 South West 11
## 851 2020-05-10 South West 5
## 852 2020-05-11 South West 8
## 853 2020-05-12 South West 7
## 854 2020-05-13 South West 7
## 855 2020-05-14 South West 6
## 856 2020-05-15 South West 4
## 857 2020-05-16 South West 4
## 858 2020-05-17 South West 6
## 859 2020-05-18 South West 4
## 860 2020-05-19 South West 6
## 861 2020-05-20 South West 1
## 862 2020-05-21 South West 9
## 863 2020-05-22 South West 6
## 864 2020-05-23 South West 6
## 865 2020-05-24 South West 3
## 866 2020-05-25 South West 8
## 867 2020-05-26 South West 11
## 868 2020-05-27 South West 5
## 869 2020-05-28 South West 10
## 870 2020-05-29 South West 7
## 871 2020-05-30 South West 3
## 872 2020-05-31 South West 2
## 873 2020-06-01 South West 7
## 874 2020-06-02 South West 2
## 875 2020-06-03 South West 7
## 876 2020-06-04 South West 2
## 877 2020-06-05 South West 2
## 878 2020-06-06 South West 1
## 879 2020-06-07 South West 3
## 880 2020-06-08 South West 3
## 881 2020-06-09 South West 0
## 882 2020-06-10 South West 1
## 883 2020-06-11 South West 2
## 884 2020-06-12 South West 2
## 885 2020-06-13 South West 2
## 886 2020-06-14 South West 0
## 887 2020-06-15 South West 1
## 888 2020-06-16 South West 2
## 889 2020-06-17 South West 0
## 890 2020-06-18 South West 0
## 891 2020-06-19 South West 0
## 892 2020-06-20 South West 2
## 893 2020-06-21 South West 0
## 894 2020-06-22 South West 1
## 895 2020-06-23 South West 1
## 896 2020-06-24 South West 1
## 897 2020-06-25 South West 0
## 898 2020-06-26 South West 3
## 899 2020-06-27 South West 0
## 900 2020-06-28 South West 0
## 901 2020-06-29 South West 1
## 902 2020-06-30 South West 0
## 903 2020-07-01 South West 0
## 904 2020-07-02 South West 0
## 905 2020-07-03 South West 0
## 906 2020-07-04 South West 0
## 907 2020-07-05 South West 1
## 908 2020-07-06 South West 0
## 909 2020-07-07 South West 0
## 910 2020-07-08 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 09 Jul 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -12.2453 -3.8030 -0.1868 3.2481 7.4240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.686e+00 6.176e-02 75.88 <2e-16 ***
## note_lag 1.378e-05 6.390e-07 21.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 20.44948)
##
## Null deviance: 10275.2 on 68 degrees of freedom
## Residual deviance: 1450.3 on 67 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 108.444584 1.000014
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 95.912972 122.189032
## note_lag 1.000013 1.000015
Rsq(lag_mod)
## [1] 0.8588503
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.2 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.4.2 webshot_0.5.2 httr_1.4.1
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-8 zip_2.0.4 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.5 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.12.8 modelr_0.1.8 vctrs_0.3.1 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.15
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1